To validate our algorithm in a meaningful setup, we need PAD datasets with a diverse set of PAs but such datasets are scarce in the literature. A secondary, yet important, contribution of our work is the introduction of the Face Presentation Attack Detection from Information Sciences Institute (PADISI-Face) dataset which includes various major face spoofing attack types. To the best of our knowledge, the only other comparable dataset that is accessible at the moment is the recently released HQ-WMCA face anti-spoofing dataset [1]}The Wild with Multiple Attacks Database (SiW-M) face anti-spoofing dataset [2]} is another existing dataset with various PA types. SiW-M dataset includes various attack types, similar to PADISI-Face. However, that SiW-M is temporarily inaccessible.
Hence, PADISI-Face can serve as a possible substitute for SiW-M. The PADISI-Face dataset is publicly available at https://github.com/ISICV/PADISI_USC_Dataset.. In PADISI-Face Dataset, each capture consists of a 60-frame sequence of \(1984 \times 1264\)  pixel images. The PADISI-Face dataset contains comparable variety of spoofing attacks to HQ-WMCA. Table REF  presents statistics of the collected dataset as well the HQ-WMCA, for comparison. Figure REF  visualizes instances of all the attack types in the dataset. For comprehensive details on the PADISI-Face dataset and its characteristics, please refer to the Appendix.
<TABLE><FIGURE>